Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations52516
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory137.0 B

Variable types

Text7
Numeric3
Categorical4
DateTime4

Alerts

lifetime is highly overall correlated with lifetime_groupHigh correlation
lifetime_group is highly overall correlated with lifetime and 1 other fieldsHigh correlation
status is highly overall correlated with lifetime_groupHigh correlation
status is highly imbalanced (55.2%) Imbalance
countries_income_group is highly imbalanced (59.3%) Imbalance
funding_total_usd is highly skewed (γ1 = 120.7777503) Skewed
name has unique values Unique

Reproduction

Analysis started2024-12-11 21:03:12.945936
Analysis finished2024-12-11 21:03:18.093337
Duration5.15 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

name
Text

Unique 

Distinct52516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:18.479653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length97
Median length62
Mean length12.101379
Min length1

Characters and Unicode

Total characters635516
Distinct characters227
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52516 ?
Unique (%)100.0%

Sample

1st rowLunchgate
2nd rowEarLens
3rd rowReviva Pharmaceuticals
4th rowSancilio and Company
5th rowWireTough Cylinders
ValueCountFrequency (%)
technologies 1229
 
1.5%
inc 1127
 
1.3%
systems 724
 
0.9%
solutions 630
 
0.7%
group 559
 
0.7%
media 528
 
0.6%
technology 509
 
0.6%
medical 478
 
0.6%
the 463
 
0.5%
networks 418
 
0.5%
Other values (48375) 77834
92.1%
2024-12-12T00:03:19.089060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 59710
 
9.4%
o 44019
 
6.9%
i 43013
 
6.8%
a 42156
 
6.6%
n 35157
 
5.5%
t 33927
 
5.3%
r 33837
 
5.3%
31981
 
5.0%
s 26632
 
4.2%
l 25020
 
3.9%
Other values (217) 260064
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 635516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 59710
 
9.4%
o 44019
 
6.9%
i 43013
 
6.8%
a 42156
 
6.6%
n 35157
 
5.5%
t 33927
 
5.3%
r 33837
 
5.3%
31981
 
5.0%
s 26632
 
4.2%
l 25020
 
3.9%
Other values (217) 260064
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 635516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 59710
 
9.4%
o 44019
 
6.9%
i 43013
 
6.8%
a 42156
 
6.6%
n 35157
 
5.5%
t 33927
 
5.3%
r 33837
 
5.3%
31981
 
5.0%
s 26632
 
4.2%
l 25020
 
3.9%
Other values (217) 260064
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 635516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 59710
 
9.4%
o 44019
 
6.9%
i 43013
 
6.8%
a 42156
 
6.6%
n 35157
 
5.5%
t 33927
 
5.3%
r 33837
 
5.3%
31981
 
5.0%
s 26632
 
4.2%
l 25020
 
3.9%
Other values (217) 260064
40.9%
Distinct22106
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:19.383166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length639
Median length248
Mean length28.061219
Min length2

Characters and Unicode

Total characters1473663
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20140 ?
Unique (%)38.4%

Sample

1st rowOnline Reservations|Restaurants
2nd rowManufacturing|Medical|Medical Devices
3rd rowBiotechnology
4th rowHealth Care
5th rowManufacturing
ValueCountFrequency (%)
software 5014
 
4.6%
and 3068
 
2.8%
biotechnology 2906
 
2.7%
no_category 2465
 
2.3%
health 2371
 
2.2%
media 2212
 
2.0%
curated 1747
 
1.6%
1550
 
1.4%
technology 1504
 
1.4%
web 1244
 
1.1%
Other values (22412) 84503
77.8%
2024-12-12T00:03:20.267712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 153667
 
10.4%
a 105223
 
7.1%
i 101104
 
6.9%
o 100039
 
6.8%
n 95579
 
6.5%
t 94208
 
6.4%
r 80897
 
5.5%
| 70937
 
4.8%
s 62364
 
4.2%
l 58518
 
4.0%
Other values (54) 551127
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1473663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 153667
 
10.4%
a 105223
 
7.1%
i 101104
 
6.9%
o 100039
 
6.8%
n 95579
 
6.5%
t 94208
 
6.4%
r 80897
 
5.5%
| 70937
 
4.8%
s 62364
 
4.2%
l 58518
 
4.0%
Other values (54) 551127
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1473663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 153667
 
10.4%
a 105223
 
7.1%
i 101104
 
6.9%
o 100039
 
6.8%
n 95579
 
6.5%
t 94208
 
6.4%
r 80897
 
5.5%
| 70937
 
4.8%
s 62364
 
4.2%
l 58518
 
4.0%
Other values (54) 551127
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1473663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 153667
 
10.4%
a 105223
 
7.1%
i 101104
 
6.9%
o 100039
 
6.8%
n 95579
 
6.5%
t 94208
 
6.4%
r 80897
 
5.5%
| 70937
 
4.8%
s 62364
 
4.2%
l 58518
 
4.0%
Other values (54) 551127
37.4%

funding_total_usd
Real number (ℝ)

Skewed 

Distinct15555
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14978122
Minimum1
Maximum3.0079503 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:20.509190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37614
Q1529721.25
median1021239
Q37000000
95-th percentile56052954
Maximum3.0079503 × 1010
Range3.0079503 × 1010
Interquartile range (IQR)6470278.8

Descriptive statistics

Standard deviation1.6836128 × 108
Coefficient of variation (CV)11.24048
Kurtosis19920.68
Mean14978122
Median Absolute Deviation (MAD)971239
Skewness120.77775
Sum7.8659106 × 1011
Variance2.8345522 × 1016
MonotonicityNot monotonic
2024-12-12T00:03:20.777199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
860000 9193
 
17.5%
1000000 899
 
1.7%
100000 786
 
1.5%
500000 772
 
1.5%
2927171 706
 
1.3%
2000000 651
 
1.2%
50000 628
 
1.2%
40000 545
 
1.0%
250000 500
 
1.0%
5000000 495
 
0.9%
Other values (15545) 37341
71.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
20 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
50 1
< 0.1%
ValueCountFrequency (%)
3.0079503 × 10101
< 0.1%
1.066494364 × 10101
< 0.1%
8207450000 1
< 0.1%
5820000000 1
< 0.1%
5800000000 1
< 0.1%
5162513431 1
< 0.1%
5150000000 1
< 0.1%
4812000000 1
< 0.1%
4745460219 1
< 0.1%
4630000000 1
< 0.1%

status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
operating
47599 
closed
4917 

Length

Max length9
Median length9
Mean length8.7191142
Min length6

Characters and Unicode

Total characters457893
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoperating
2nd rowoperating
3rd rowoperating
4th rowoperating
5th rowoperating

Common Values

ValueCountFrequency (%)
operating 47599
90.6%
closed 4917
 
9.4%

Length

2024-12-12T00:03:21.013762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-12T00:03:21.161950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
operating 47599
90.6%
closed 4917
 
9.4%

Most occurring characters

ValueCountFrequency (%)
o 52516
11.5%
e 52516
11.5%
p 47599
10.4%
r 47599
10.4%
a 47599
10.4%
t 47599
10.4%
i 47599
10.4%
n 47599
10.4%
g 47599
10.4%
c 4917
 
1.1%
Other values (3) 14751
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 457893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 52516
11.5%
e 52516
11.5%
p 47599
10.4%
r 47599
10.4%
a 47599
10.4%
t 47599
10.4%
i 47599
10.4%
n 47599
10.4%
g 47599
10.4%
c 4917
 
1.1%
Other values (3) 14751
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 457893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 52516
11.5%
e 52516
11.5%
p 47599
10.4%
r 47599
10.4%
a 47599
10.4%
t 47599
10.4%
i 47599
10.4%
n 47599
10.4%
g 47599
10.4%
c 4917
 
1.1%
Other values (3) 14751
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 457893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 52516
11.5%
e 52516
11.5%
p 47599
10.4%
r 47599
10.4%
a 47599
10.4%
t 47599
10.4%
i 47599
10.4%
n 47599
10.4%
g 47599
10.4%
c 4917
 
1.1%
Other values (3) 14751
 
3.2%
Distinct135
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:21.357291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.7333765
Min length3

Characters and Unicode

Total characters196062
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowCHE
2nd rowUSA
3rd rowUSA
4th rowno_country
5th rowUSA
ValueCountFrequency (%)
usa 29702
56.6%
no_country 5502
 
10.5%
gbr 2925
 
5.6%
can 1540
 
2.9%
ind 1276
 
2.4%
chn 1240
 
2.4%
fra 916
 
1.7%
deu 834
 
1.6%
isr 767
 
1.5%
esp 607
 
1.2%
Other values (125) 7207
 
13.7%
2024-12-12T00:03:21.727474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 33855
17.3%
S 32775
16.7%
U 31752
16.2%
n 11004
 
5.6%
o 11004
 
5.6%
R 6858
 
3.5%
N 5773
 
2.9%
r 5502
 
2.8%
y 5502
 
2.8%
t 5502
 
2.8%
Other values (24) 46535
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 33855
17.3%
S 32775
16.7%
U 31752
16.2%
n 11004
 
5.6%
o 11004
 
5.6%
R 6858
 
3.5%
N 5773
 
2.9%
r 5502
 
2.8%
y 5502
 
2.8%
t 5502
 
2.8%
Other values (24) 46535
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 33855
17.3%
S 32775
16.7%
U 31752
16.2%
n 11004
 
5.6%
o 11004
 
5.6%
R 6858
 
3.5%
N 5773
 
2.9%
r 5502
 
2.8%
y 5502
 
2.8%
t 5502
 
2.8%
Other values (24) 46535
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 33855
17.3%
S 32775
16.7%
U 31752
16.2%
n 11004
 
5.6%
o 11004
 
5.6%
R 6858
 
3.5%
N 5773
 
2.9%
r 5502
 
2.8%
y 5502
 
2.8%
t 5502
 
2.8%
Other values (24) 46535
23.7%
Distinct301
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:22.129011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.694893
Min length1

Characters and Unicode

Total characters141525
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.1%

Sample

1st row25
2nd rowCA
3rd rowCA
4th rowno_state
5th rowVA
ValueCountFrequency (%)
ca 10219
19.5%
no_state 6763
 
12.9%
ny 3112
 
5.9%
ma 2020
 
3.8%
tx 1562
 
3.0%
h9 1510
 
2.9%
7 1095
 
2.1%
fl 1038
 
2.0%
wa 997
 
1.9%
2 913
 
1.7%
Other values (291) 23287
44.3%
2024-12-12T00:03:22.689313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 16811
 
11.9%
t 13526
 
9.6%
C 12894
 
9.1%
n 6763
 
4.8%
o 6763
 
4.8%
_ 6763
 
4.8%
s 6763
 
4.8%
a 6763
 
4.8%
e 6763
 
4.8%
N 6498
 
4.6%
Other values (33) 51218
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 16811
 
11.9%
t 13526
 
9.6%
C 12894
 
9.1%
n 6763
 
4.8%
o 6763
 
4.8%
_ 6763
 
4.8%
s 6763
 
4.8%
a 6763
 
4.8%
e 6763
 
4.8%
N 6498
 
4.6%
Other values (33) 51218
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 16811
 
11.9%
t 13526
 
9.6%
C 12894
 
9.1%
n 6763
 
4.8%
o 6763
 
4.8%
_ 6763
 
4.8%
s 6763
 
4.8%
a 6763
 
4.8%
e 6763
 
4.8%
N 6498
 
4.6%
Other values (33) 51218
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 16811
 
11.9%
t 13526
 
9.6%
C 12894
 
9.1%
n 6763
 
4.8%
o 6763
 
4.8%
_ 6763
 
4.8%
s 6763
 
4.8%
a 6763
 
4.8%
e 6763
 
4.8%
N 6498
 
4.6%
Other values (33) 51218
36.2%

region
Text

Distinct1037
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:23.072920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length38
Median length26
Mean length9.2023955
Min length3

Characters and Unicode

Total characters483273
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)0.4%

Sample

1st rowZurich
2nd rowSF Bay Area
3rd rowSF Bay Area
4th rowno_region
5th rowVA - Other
ValueCountFrequency (%)
bay 6992
 
8.1%
sf 6970
 
8.1%
area 6970
 
8.1%
no_region 6359
 
7.4%
city 3404
 
3.9%
new 3285
 
3.8%
york 2859
 
3.3%
2825
 
3.3%
other 2556
 
3.0%
boston 1891
 
2.2%
Other values (1135) 42302
49.0%
2024-12-12T00:03:23.725676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 42283
 
8.7%
o 41619
 
8.6%
a 39740
 
8.2%
n 38761
 
8.0%
33897
 
7.0%
r 30705
 
6.4%
i 27327
 
5.7%
t 20348
 
4.2%
l 15248
 
3.2%
g 15119
 
3.1%
Other values (57) 178226
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 483273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 42283
 
8.7%
o 41619
 
8.6%
a 39740
 
8.2%
n 38761
 
8.0%
33897
 
7.0%
r 30705
 
6.4%
i 27327
 
5.7%
t 20348
 
4.2%
l 15248
 
3.2%
g 15119
 
3.1%
Other values (57) 178226
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 483273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 42283
 
8.7%
o 41619
 
8.6%
a 39740
 
8.2%
n 38761
 
8.0%
33897
 
7.0%
r 30705
 
6.4%
i 27327
 
5.7%
t 20348
 
4.2%
l 15248
 
3.2%
g 15119
 
3.1%
Other values (57) 178226
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 483273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 42283
 
8.7%
o 41619
 
8.6%
a 39740
 
8.2%
n 38761
 
8.0%
33897
 
7.0%
r 30705
 
6.4%
i 27327
 
5.7%
t 20348
 
4.2%
l 15248
 
3.2%
g 15119
 
3.1%
Other values (57) 178226
36.9%

city
Text

Distinct4478
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:24.157315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length27
Mean length8.4619545
Min length2

Characters and Unicode

Total characters444388
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2304 ?
Unique (%)4.4%

Sample

1st rowZürich
2nd rowRedwood City
3rd rowSan Jose
4th rowno_city
5th rowBristol
ValueCountFrequency (%)
no_city 6359
 
9.3%
san 4282
 
6.3%
francisco 2884
 
4.2%
new 2756
 
4.0%
york 2493
 
3.6%
london 1521
 
2.2%
city 845
 
1.2%
los 727
 
1.1%
santa 710
 
1.0%
palo 612
 
0.9%
Other values (4361) 45191
66.1%
2024-12-12T00:03:24.793564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 41983
 
9.4%
o 41535
 
9.3%
a 39037
 
8.8%
e 30594
 
6.9%
i 30444
 
6.9%
t 23970
 
5.4%
r 22491
 
5.1%
l 20382
 
4.6%
c 17296
 
3.9%
s 15911
 
3.6%
Other values (78) 160745
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 444388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 41983
 
9.4%
o 41535
 
9.3%
a 39037
 
8.8%
e 30594
 
6.9%
i 30444
 
6.9%
t 23970
 
5.4%
r 22491
 
5.1%
l 20382
 
4.6%
c 17296
 
3.9%
s 15911
 
3.6%
Other values (78) 160745
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 444388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 41983
 
9.4%
o 41535
 
9.3%
a 39037
 
8.8%
e 30594
 
6.9%
i 30444
 
6.9%
t 23970
 
5.4%
r 22491
 
5.1%
l 20382
 
4.6%
c 17296
 
3.9%
s 15911
 
3.6%
Other values (78) 160745
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 444388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 41983
 
9.4%
o 41535
 
9.3%
a 39037
 
8.8%
e 30594
 
6.9%
i 30444
 
6.9%
t 23970
 
5.4%
r 22491
 
5.1%
l 20382
 
4.6%
c 17296
 
3.9%
s 15911
 
3.6%
Other values (78) 160745
36.2%

funding_rounds
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7406695
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:24.965098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum19
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3745222
Coefficient of variation (CV)0.78965143
Kurtosis14.293641
Mean1.7406695
Median Absolute Deviation (MAD)0
Skewness3.0690907
Sum91413
Variance1.8893112
MonotonicityNot monotonic
2024-12-12T00:03:25.107259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 33489
63.8%
2 9816
 
18.7%
3 4450
 
8.5%
4 2189
 
4.2%
5 1168
 
2.2%
6 616
 
1.2%
7 341
 
0.6%
8 175
 
0.3%
9 109
 
0.2%
10 63
 
0.1%
Other values (9) 100
 
0.2%
ValueCountFrequency (%)
1 33489
63.8%
2 9816
 
18.7%
3 4450
 
8.5%
4 2189
 
4.2%
5 1168
 
2.2%
6 616
 
1.2%
7 341
 
0.6%
8 175
 
0.3%
9 109
 
0.2%
10 63
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 6
 
< 0.1%
15 5
 
< 0.1%
14 5
 
< 0.1%
13 9
 
< 0.1%
12 22
 
< 0.1%
11 46
0.1%
10 63
0.1%
Distinct5402
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
Minimum1970-02-05 00:00:00
Maximum2016-04-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-12T00:03:25.280666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:25.448224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4603
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
Minimum1977-05-15 00:00:00
Maximum2015-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-12T00:03:25.656334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:25.850134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4305
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
Minimum1977-05-15 00:00:00
Maximum2015-12-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-12T00:03:26.129237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:26.376282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3009
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
Minimum1983-10-06 00:00:00
Maximum2018-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-12T00:03:26.561307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:26.750255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

lifetime
Real number (ℝ)

High correlation 

Distinct5738
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3140.8281
Minimum19
Maximum17378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:26.945733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile991
Q11739
median2557
Q34018
95-th percentile7277.5
Maximum17378
Range17359
Interquartile range (IQR)2279

Descriptive statistics

Standard deviation2147.8211
Coefficient of variation (CV)0.6838391
Kurtosis5.840143
Mean3140.8281
Median Absolute Deviation (MAD)1004
Skewness2.0047053
Sum1.6494373 × 108
Variance4613135.3
MonotonicityNot monotonic
2024-12-12T00:03:27.134112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2192 2069
 
3.9%
1826 1896
 
3.6%
2557 1882
 
3.6%
2922 1556
 
3.0%
3287 1370
 
2.6%
1461 1225
 
2.3%
4018 1096
 
2.1%
3653 1030
 
2.0%
4383 894
 
1.7%
4748 801
 
1.5%
Other values (5728) 38697
73.7%
ValueCountFrequency (%)
19 1
< 0.1%
57 1
< 0.1%
58 1
< 0.1%
70 1
< 0.1%
71 1
< 0.1%
90 1
< 0.1%
112 1
< 0.1%
119 1
< 0.1%
122 1
< 0.1%
126 1
< 0.1%
ValueCountFrequency (%)
17378 1
 
< 0.1%
17167 12
< 0.1%
17079 1
 
< 0.1%
17004 1
 
< 0.1%
16802 13
< 0.1%
16711 1
 
< 0.1%
16609 1
 
< 0.1%
16436 9
< 0.1%
16346 1
 
< 0.1%
16316 1
 
< 0.1%

lifetime_group
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
>5 лет
38183 
3-5 лет
10854 
1-3 лет
 
3395
<1 года
 
84

Length

Max length7
Median length6
Mean length6.2729263
Min length6

Characters and Unicode

Total characters329429
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>5 лет
2nd row>5 лет
3rd row>5 лет
4th row>5 лет
5th row>5 лет

Common Values

ValueCountFrequency (%)
>5 лет 38183
72.7%
3-5 лет 10854
 
20.7%
1-3 лет 3395
 
6.5%
<1 года 84
 
0.2%

Length

2024-12-12T00:03:27.284376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-12T00:03:27.439300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
лет 52432
49.9%
5 38183
36.4%
3-5 10854
 
10.3%
1-3 3395
 
3.2%
1 84
 
0.1%
года 84
 
0.1%

Most occurring characters

ValueCountFrequency (%)
52516
15.9%
л 52432
15.9%
е 52432
15.9%
т 52432
15.9%
5 49037
14.9%
> 38183
11.6%
3 14249
 
4.3%
- 14249
 
4.3%
1 3479
 
1.1%
< 84
 
< 0.1%
Other values (4) 336
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 329429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
52516
15.9%
л 52432
15.9%
е 52432
15.9%
т 52432
15.9%
5 49037
14.9%
> 38183
11.6%
3 14249
 
4.3%
- 14249
 
4.3%
1 3479
 
1.1%
< 84
 
< 0.1%
Other values (4) 336
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 329429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
52516
15.9%
л 52432
15.9%
е 52432
15.9%
т 52432
15.9%
5 49037
14.9%
> 38183
11.6%
3 14249
 
4.3%
- 14249
 
4.3%
1 3479
 
1.1%
< 84
 
< 0.1%
Other values (4) 336
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 329429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
52516
15.9%
л 52432
15.9%
е 52432
15.9%
т 52432
15.9%
5 49037
14.9%
> 38183
11.6%
3 14249
 
4.3%
- 14249
 
4.3%
1 3479
 
1.1%
< 84
 
< 0.1%
Other values (4) 336
 
0.1%
Distinct708
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
2024-12-12T00:03:27.700539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length37
Median length29
Mean length11.315313
Min length2

Characters and Unicode

Total characters594235
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)0.1%

Sample

1st rowonline reservations
2nd rowmanufacturing
3rd rowbiotechnology
4th rowhealth care
5th rowmanufacturing
ValueCountFrequency (%)
software 5342
 
7.3%
biotechnology 3593
 
4.9%
no_category 2465
 
3.4%
health 2257
 
3.1%
e-commerce 2203
 
3.0%
web 2054
 
2.8%
advertising 2050
 
2.8%
technology 1834
 
2.5%
curated 1747
 
2.4%
analytics 1542
 
2.1%
Other values (674) 47791
65.6%
2024-12-12T00:03:28.199311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 68873
11.6%
o 48621
 
8.2%
a 48045
 
8.1%
t 45957
 
7.7%
n 42875
 
7.2%
i 39652
 
6.7%
r 36967
 
6.2%
c 36608
 
6.2%
s 34490
 
5.8%
l 25002
 
4.2%
Other values (27) 167145
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 68873
11.6%
o 48621
 
8.2%
a 48045
 
8.1%
t 45957
 
7.7%
n 42875
 
7.2%
i 39652
 
6.7%
r 36967
 
6.2%
c 36608
 
6.2%
s 34490
 
5.8%
l 25002
 
4.2%
Other values (27) 167145
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 68873
11.6%
o 48621
 
8.2%
a 48045
 
8.1%
t 45957
 
7.7%
n 42875
 
7.2%
i 39652
 
6.7%
r 36967
 
6.2%
c 36608
 
6.2%
s 34490
 
5.8%
l 25002
 
4.2%
Other values (27) 167145
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 68873
11.6%
o 48621
 
8.2%
a 48045
 
8.1%
t 45957
 
7.7%
n 42875
 
7.2%
i 39652
 
6.7%
r 36967
 
6.2%
c 36608
 
6.2%
s 34490
 
5.8%
l 25002
 
4.2%
Other values (27) 167145
28.1%

activity_group
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
J
19831 
C
8187 
M
4694 
R
2950 
Q
2908 
Other values (15)
13946 

Length

Max length11
Median length1
Mean length1.4693808
Min length1

Characters and Unicode

Total characters77166
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ
2nd rowC
3rd rowC
4th rowQ
5th rowC

Common Values

ValueCountFrequency (%)
J 19831
37.8%
C 8187
15.6%
M 4694
 
8.9%
R 2950
 
5.6%
Q 2908
 
5.5%
S 2755
 
5.2%
G 2727
 
5.2%
NO_CATEGORY 2465
 
4.7%
K 2015
 
3.8%
P 1238
 
2.4%
Other values (10) 2746
 
5.2%

Length

2024-12-12T00:03:28.424519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j 19831
37.8%
c 8187
15.6%
m 4694
 
8.9%
r 2950
 
5.6%
q 2908
 
5.5%
s 2755
 
5.2%
g 2727
 
5.2%
no_category 2465
 
4.7%
k 2015
 
3.8%
p 1238
 
2.4%
Other values (10) 2746
 
5.2%

Most occurring characters

ValueCountFrequency (%)
J 19831
25.7%
C 10652
13.8%
R 5415
 
7.0%
G 5192
 
6.7%
O 5000
 
6.5%
M 4694
 
6.1%
N 3057
 
4.0%
Q 2908
 
3.8%
S 2755
 
3.6%
A 2613
 
3.4%
Other values (12) 15049
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 19831
25.7%
C 10652
13.8%
R 5415
 
7.0%
G 5192
 
6.7%
O 5000
 
6.5%
M 4694
 
6.1%
N 3057
 
4.0%
Q 2908
 
3.8%
S 2755
 
3.6%
A 2613
 
3.4%
Other values (12) 15049
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 19831
25.7%
C 10652
13.8%
R 5415
 
7.0%
G 5192
 
6.7%
O 5000
 
6.5%
M 4694
 
6.1%
N 3057
 
4.0%
Q 2908
 
3.8%
S 2755
 
3.6%
A 2613
 
3.4%
Other values (12) 15049
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 19831
25.7%
C 10652
13.8%
R 5415
 
7.0%
G 5192
 
6.7%
O 5000
 
6.5%
M 4694
 
6.1%
N 3057
 
4.0%
Q 2908
 
3.8%
S 2755
 
3.6%
A 2613
 
3.4%
Other values (12) 15049
19.5%

countries_income_group
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size410.4 KiB
High income
42920 
NO_COUNTRY
5502 
Upper middle income
 
2463
Lower middle income
 
1611
Low income
 
20

Length

Max length19
Median length11
Mean length11.515462
Min length10

Characters and Unicode

Total characters604746
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh income
2nd rowHigh income
3rd rowHigh income
4th rowNO_COUNTRY
5th rowHigh income

Common Values

ValueCountFrequency (%)
High income 42920
81.7%
NO_COUNTRY 5502
 
10.5%
Upper middle income 2463
 
4.7%
Lower middle income 1611
 
3.1%
Low income 20
 
< 0.1%

Length

2024-12-12T00:03:28.903403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-12T00:03:29.140384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
income 47014
45.4%
high 42920
41.4%
no_country 5502
 
5.3%
middle 4074
 
3.9%
upper 2463
 
2.4%
lower 1611
 
1.6%
low 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 94008
15.5%
e 55162
9.1%
51088
8.4%
m 51088
8.4%
o 48645
8.0%
n 47014
7.8%
c 47014
7.8%
H 42920
7.1%
g 42920
7.1%
h 42920
7.1%
Other values (14) 81967
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 94008
15.5%
e 55162
9.1%
51088
8.4%
m 51088
8.4%
o 48645
8.0%
n 47014
7.8%
c 47014
7.8%
H 42920
7.1%
g 42920
7.1%
h 42920
7.1%
Other values (14) 81967
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 94008
15.5%
e 55162
9.1%
51088
8.4%
m 51088
8.4%
o 48645
8.0%
n 47014
7.8%
c 47014
7.8%
H 42920
7.1%
g 42920
7.1%
h 42920
7.1%
Other values (14) 81967
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 94008
15.5%
e 55162
9.1%
51088
8.4%
m 51088
8.4%
o 48645
8.0%
n 47014
7.8%
c 47014
7.8%
H 42920
7.1%
g 42920
7.1%
h 42920
7.1%
Other values (14) 81967
13.6%

Interactions

2024-12-12T00:03:16.813307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:15.932710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.394548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.969614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.082218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.543293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:17.109254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.243332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-12T00:03:16.695617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-12-12T00:03:29.283643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
activity_groupcountries_income_groupfunding_roundsfunding_total_usdlifetimelifetime_groupstatus
activity_group1.0000.1580.0460.0150.0760.1190.162
countries_income_group0.1581.0000.0620.0140.0870.1130.166
funding_rounds0.0460.0621.0000.4670.2150.1130.076
funding_total_usd0.0150.0140.4671.0000.4070.0000.000
lifetime0.0760.0870.2150.4071.0000.5500.413
lifetime_group0.1190.1130.1130.0000.5501.0000.666
status0.1620.1660.0760.0000.4130.6661.000

Missing values

2024-12-12T00:03:17.356428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-12T00:03:17.839075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namecategory_listfunding_total_usdstatuscountry_codestate_coderegioncityfunding_roundsfounded_atfirst_funding_atlast_funding_atclosed_atlifetimelifetime_groupcategory_first_wordactivity_groupcountries_income_group
0LunchgateOnline Reservations|Restaurants828626.0operatingCHE25ZurichZürich22009-10-172011-05-012014-12-012018-01-012998>5 летonline reservationsJHigh income
1EarLensManufacturing|Medical|Medical Devices42935019.0operatingUSACASF Bay AreaRedwood City42005-01-012010-05-042014-02-252018-01-014748>5 летmanufacturingCHigh income
2Reviva PharmaceuticalsBiotechnology35456381.0operatingUSACASF Bay AreaSan Jose32006-01-012012-08-202014-07-022018-01-014383>5 летbiotechnologyCHigh income
3Sancilio and CompanyHealth Care22250000.0operatingno_countryno_stateno_regionno_city32004-01-012011-09-012014-07-182018-01-015114>5 летhealth careQNO_COUNTRY
4WireTough CylindersManufacturing860000.0operatingUSAVAVA - OtherBristol12010-05-122012-02-012012-02-012018-01-012791>5 летmanufacturingCHigh income
5Connected Sports VenturesMobile4300000.0operatingUSANJNewarkPrinceton12011-04-162012-11-122012-11-122018-01-012452>5 летmobileJHigh income
6AttensityAnalytics|Business Analytics|Social CRM|Social Media Monitoring|Software90000000.0operatingUSACASF Bay AreaRedwood City12000-01-012014-05-142014-05-142018-01-016575>5 летanalyticsJHigh income
7Mesh NetworksSoftware4300000.0operatingUSATXHoustonHouston12005-01-012014-11-092014-11-092018-01-014748>5 летsoftwareJHigh income
8AngioScoreBiotechnology42000000.0operatingUSACASF Bay AreaFremont22003-01-012007-10-092011-04-202018-01-015479>5 летbiotechnologyCHigh income
9VidatronicSemiconductors1250500.0operatingUSATXAustinCollege Station22010-01-012011-08-232013-03-212018-01-012922>5 летsemiconductorsCHigh income
namecategory_listfunding_total_usdstatuscountry_codestate_coderegioncityfunding_roundsfounded_atfirst_funding_atlast_funding_atclosed_atlifetimelifetime_groupcategory_first_wordactivity_groupcountries_income_group
52506Dignify TherapeuticsBiotechnology3209000.0operatingUSANCRaleighRaleigh32013-01-012014-02-052015-02-042018-01-011826>5 летbiotechnologyCHigh income
52507Proactive ComfortMedical860000.0operatingUSAMDBaltimoreCentreville12009-10-162011-01-202011-01-202018-01-012999>5 летmedicalQHigh income
52508ScramCardno_category860000.0operatingHKGno_stateHong KongHong Kong12013-01-012015-11-202015-11-202018-01-011826>5 летno_categoryNO_CATEGORYHigh income
52509Visionary MobileBiotechnology1570000.0operatingUSAORSalem, OregonCorvallis32010-01-012011-09-202012-04-112018-01-012922>5 летbiotechnologyCHigh income
52510Shanghai Media GroupNews205600000.0operatingCHN23ShanghaiShanghai22001-01-012008-04-152015-06-042018-01-016209>5 летnewsJUpper middle income
52511VideostreamEntertainment860000.0operatingCANONTorontoKitchener12012-01-012014-03-012014-03-012018-01-012192>5 летentertainmentRHigh income
52512Hello CurryHospitality500000.0operatingIND2HyderabadHyderabad12013-08-252014-03-072014-03-072018-01-0115903-5 летhospitalityILower middle income
52513TaskforceEmail|Messaging|Productivity Software50000.0operatingUSACASF Bay AreaSan Francisco32010-07-012009-06-142011-01-012018-01-012741>5 летemailJHigh income
52514NetScalerSecurity13000000.0operatingUSACASF Bay AreaSan Jose61997-12-011998-11-302004-03-012018-01-017336>5 летsecurityJHigh income
52515ApparcandoOnline Rental|Parking|Price Comparison270820.0operatingESP60ValenciaValencia12012-08-112014-06-132014-06-132018-01-011969>5 летonline rentalGHigh income